def get_model_for_problem_formulation(problem_formulation_idea):
disease_model = VensimModel("multidisease",
wd=r'.\models',
model_file='disease_model.vpm')
disease_model.uncertainties = [
# Sanitation Unknowns
IntegerParameter('desire for improved sanitation', 10, 100),
# Water Supply Unknowns
IntegerParameter('Cost of well repair', 660, 1800),
# Water Quality Unknowns
IntegerParameter('use HWT', 10, 100),
# Hygiene Unknowns
IntegerParameter('Intensity of hygiene campaign', 10, 100),
# Vaccination Unknowns
IntegerParameter('Reliability of vaccine supply', 10, 100),
# Treatment Unknowns
IntegerParameter('seeking treatment', 10, 100),
# Other Unknowns
IntegerParameter('percent willing to accept MDA', 10, 100),
# RealParameter('Childbearing years', 9, 14), #N.B. huge impact
]
disease_model.constants = [
Constant('Length latrine program', 10),
Constant('Length GW supply program', 10),
Constant('Length of water quality program', 10),
Constant("Duration of hygiene campaign", 10),
Constant("Length of ORT subsidy", 10),
Constant("Years of MDA campaign", 10)
]
disease_model.levers = [
# Sanitation Levers
IntegerParameter("Number of new latrines to build", 0, 9000),
IntegerParameter("Number of latrines to maintain", 0, 4000),
# Water Supply Levers
IntegerParameter("Number of new wells to drill", 0, 2000),
IntegerParameter("Number of wells to repair", 0, 2000),
# Water Quality Levers
IntegerParameter("Availability HWT", 0, 100),
# Hygiene Promotion Levers
IntegerParameter("HW stations to build", 0, 8000),
# Vaccination Levers
IntegerParameter("percentage of infants to vaccinate", 0, 100),
# Treatment Levers
IntegerParameter("Access to tmt", 0, 100),
# MDA levers
IntegerParameter("percent adults given MDA", 0, 100),
IntegerParameter("percent youth given Albendazole", 0, 100),
]
# add policies
disease_model.policies = [
Policy(
'LatrineProgram', **{
"Number of new latrines to build": 5000,
"Number of latrines to maintain": 4000,
"Length latrine program": 10,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'GWsupply', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 1000,
"Number of wells to repair": 100,
"Length GW supply program": 10,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'ORT', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 100,
"Length of ORT subsidy": 10,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'Hygiene', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 1000,
"Duration of hygiene campaign": 10,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'Vaccin', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 100,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'DrinkingWater', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 100,
"Length of water quality program": 10,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
Policy(
'MDA', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 100,
"percent youth given Albendazole": 100,
"Years of MDA campaign": 10,
}),
Policy(
'DoNothing', **{
"Number of new latrines to build": 0,
"Number of latrines to maintain": 0,
"Length latrine program": 0,
"Number of new wells to drill": 0,
"Number of wells to repair": 0,
"Length GW supply program": 0,
"Availability HWT": 0,
"Length of water quality program": 0,
"HW stations to build": 0,
"Duration of hygiene campaign": 0,
"percentage of infants to vaccinate": 0,
"Access to tmt": 0,
"Length of ORT subsidy": 0,
"percent adults given Albendazole": 0,
"percent youth given Albendazole": 0,
"Years of MDA campaign": 0,
}),
]
# Problem formulations:
direction = ScalarOutcome.MINIMIZE
if problem_formulation_idea == 1: ##PF1: Minimum child (<5 yo) deaths due to Rotavirus
disease_model.name = 'Minimize Child <5 Rotavirus infections by 2030'
disease_model.outcomes.clear()
disease_model.outcomes = [
ScalarOutcome(
'Mortality', #Deaths due to Rotavirus
variable_name=[
'children under 5 deaths[Rota]', 'ts until 2020',
'ts at 2030'
],
function=avg_over_period,
kind=direction,
expected_range=(10000, 250000)),
ScalarOutcome(
'Morbidity', #Rota DALYs children
variable_name=[
"Years Lost to Disability in Children[Rota]",
'ts until 2020', 'ts at 2030'
],
function=avg_over_period,
kind=direction,
expected_range=(6000, 9000)),
ScalarOutcome(
'Timeliness', #Delta child infections 2030
variable_name=[
"Number of Children Infected[Rota]", 'ts until 2020',
'ts at 2030'
],
function=change_over_period,
kind=direction,
expected_range=(-0.9, 0.1)),
ScalarOutcome(
'CapEx',
variable_name=['Upfront Cost', 'ts until 2020', 'ts at 2040'],
function=total_over_period,
kind=direction,
expected_range=(0, 3000000000000)),
ScalarOutcome(
'OpEx', #Recurring Cost
variable_name=[
'Recurring Cost', 'ts until 2020', 'ts at 2040'
],
function=total_over_period,
kind=direction,
expected_range=(0, 2000000000000)),
]
elif problem_formulation_idea == 2: ##PF2: Minimum prevalence of ascariasis in Youth (Infants, PreSACs, and SACs) in 5 years
disease_model.name = 'Minimize Ascariasis in Youth by 2025'
disease_model.outcomes.clear()
disease_model.outcomes = [
ScalarOutcome('Mortality',
variable_name=[
'Youth Mortality[Ascar]', 'ts until 2020',
'ts at 2025'
],
function=avg_over_period,
kind=direction,
expected_range=(1000, 20000)),
ScalarOutcome('Morbidity',
variable_name=[
'Years Lost to Disability in Youth[Ascar]',
'ts until 2020', 'ts at 2025'
],
function=avg_over_period,
kind=direction,
expected_range=(20000, 160000)),
ScalarOutcome(
'Timeliness', #Change in prevalence of ascariasis in youth by 2025
variable_name=[
'Number of Youth Infected[Ascar]', 'ts until 2020',
'ts at 2025'
],
function=change_over_period,
kind=direction,
expected_range=(-1, 0)),
ScalarOutcome(
'CapEx', #Upfront Cost
variable_name=['Upfront Cost', 'ts until 2020', 'ts at 2040'],
function=total_over_period,
kind=direction,
expected_range=(0, 3000000000000)),
ScalarOutcome(
'OpEx', #Recurring Cost
variable_name=[
'Recurring Cost', 'ts until 2020', 'ts at 2040'
],
function=total_over_period,
kind=direction,
expected_range=(0, 2000000000000)),
]
elif problem_formulation_idea == 3: #PF3: Minimum Child (<5 yo) mortality, all diseases, w/in one year
disease_model.name = 'Immediately minimize Child <5 burden from all causes'
disease_model.outcomes.clear()
disease_model.outcomes = [
ScalarOutcome('Mortality',
variable_name=[
'Total children under 5 deaths', 'ts until 2020',
'ts at 2021'
],
function=avg_over_period,
kind=direction,
expected_range=(50000, 400000)),
ScalarOutcome('Morbidity',
variable_name=[
'morbidity in children', 'ts until 2020',
'ts at 2021'
],
function=avg_over_period,
kind=direction,
expected_range=(40000, 100000)),
ScalarOutcome(
'Timeliness', #Delta child infections 2021
variable_name=[
'Total children w gastroenteric infection',
'ts until 2020', 'ts at 2021'
],
function=change_over_period,
kind=direction,
expected_range=(-0.5, 0)),
ScalarOutcome(
'CapEx', #Upfront Cost
variable_name=['Upfront Cost', 'ts until 2020', 'ts at 2040'],
function=total_over_period,
kind=direction,
expected_range=(5000000, 3000000000000)),
ScalarOutcome(
'OpEx', #Recurring Cost
variable_name=[
'Recurring Cost', 'ts until 2020', 'ts at 2040'
], #bc no rec cost will show up in 1 yr
function=total_over_period,
kind=direction,
expected_range=(50000000000, 2000000000000)),
]
elif problem_formulation_idea == 4: #PF4: Minimum number infected, all diseases, sustainably
disease_model.name = 'Minimize number infected all diseases by 2040'
disease_model.outcomes.clear()
disease_model.outcomes = [
ScalarOutcome('Mortality',
variable_name=[
'Total lives lost', 'ts until 2020', 'ts at 2040'
],
function=avg_over_period,
kind=direction,
expected_range=(50000, 250000)),
ScalarOutcome('Morbidity',
variable_name=[
'disability burden', 'ts until 2020',
'ts at 2040'
],
function=avg_over_period,
kind=direction,
expected_range=(100000, 900000)),
ScalarOutcome(
'Timeliness', #delta infections 2040
variable_name=[
'Total number of gastroenteric infection', 'ts until 2020',
'ts at 2040'
],
function=change_over_period,
kind=direction,
expected_range=(-1, -.45)),
ScalarOutcome(
'CapEx', #Upfront Cost
variable_name=['Upfront Cost', 'ts until 2020', 'ts at 2040'],
function=total_over_period,
kind=direction,
expected_range=(20000000000, 3000000000000)),
ScalarOutcome(
'OpEx', #Recurring Cost
variable_name=[
'Recurring Cost', 'ts until 2020', 'ts at 2040'
],
function=total_over_period,
kind=direction,
expected_range=(20000000000, 2000000000000)),
##recurring costs divided by 20 years
]
else:
raise TypeError('unknown problem identifier')
return disease_model
RealParameter("Lever Public Info", 0.00001, 1),
RealParameter("Lever Public Edu", 0.00001, 1),
RealParameter("Lever Facemasks", 0.00001, 1),
RealParameter("Lever Tracing", 0.00001, 1),
RealParameter("Lever Case Isolation", 0.00001, 1),
RealParameter("Lever Voluntary or Involuntary Vaccination", 0.00001, 1),
RealParameter("Lever Vaccination", 0.00001, 1),
RealParameter("Lever Medical Care I", 0.00001, 1),
RealParameter("Lever Medical Care II", 0.00001, 1)
]
mdl = VensimModel('Immunization',
model_file=r'./models/03_SEIR_SIR_ActionsAsLevers.vpm')
mdl.uncertainties = uncertainties
mdl.outcomes = outcomes
mdl.constants = constants
mdl.levers = levers
mdl.robustness = robustness
mdl.time_horizon = 360
n_scenarios = 50
scenarios = sample_uncertainties(mdl, n_scenarios)
nfe = 1000
with SequentialEvaluator(mdl) as evaluator:
robust_results = evaluator.robust_optimize(mdl.robustness,
scenarios,
nfe=nfe,
epsilons=[
0.1,
] * len(mdl.robustness),
df_unc['Min'] = df_unc['Reference'] * 0.5
df_unc['Max'] = df_unc['Reference'] * 1.5
vensimModel.uncertainties = [
RealParameter(row['Uncertainties'], row['Min'], row['Max'])
for index, row in df_unc.iterrows()
]
#vensimModel.outcomes = [TimeSeriesOutcome(out) for out in df_out['Outcomes']]
vensimModel.outcomes = [
TimeSeriesOutcome('Total Agricultural and Land Use Emissions')
]
sc = 0
n = 2500
vensimModel.constants = [Constant('SA Diet Composition Switch', sc)]
with MultiprocessingEvaluator(vensimModel, n_processes=7) as evaluator:
for sc in [0, 2, 3, 4]:
start = time.time()
results_sa = evaluator.perform_experiments(
n, uncertainty_sampling=SOBOL, reporting_interval=5000)
end = time.time()
print("Experiments took {} seconds, {} hours.".format(
end - start, (end - start) / 3600))
fn = './Diet_Sobol_n{}_sc{}_v4_2050.tar.gz'.format(
n, sc
) #v2 is with narrow ranges for efficacy and removing some of the unimportant parameters
#v3 is with the new multiplicative formulation, and new social norm parameters
save_results(results_sa, fn)